Senior DevOps Engineer

DocuSketch

WorldwideremotePosted 2 months ago

Skill Required

DevOps-EngineerPlatform-EngineerSRECloud-Infrastructure-EngineerDevOps-LeadSenior-DevOpsSenior-DevOps-DeveloperSenior-Staff-DevOps-EngineerSenior-DevOps-Platform-EngineerSenior-AWS-DevOps-EngineerSenior-DevOps-ManagerSenior-DevOps-ArchitectDevOps EngineerDevOpsBackup and RecoverySOCGitHub Actionsrelated fieldObservabilityEngineeringR ProgrammingKubernetesautomationTerraformdesigningsecurityTestNGPythondesignArgoCDGitCI/CDCloudHelmShell ScriptingAWSandGoFulltime

Key highlights

Requires 5+ years of professional experience in DevOps or related fields.
Expert‑level experience with Kubernetes and Infrastructure‑as‑Code is mandatory.
Strong knowledge of AWS multi‑account environments is required.
Ability to use AI tools to improve reliability is a key requirement.
Proven leadership of cross‑team infrastructure initiatives is essential.
Professional fluency in English is mandatory.

Role overview

Responsibilities

Lead complex infrastructure and delivery initiatives across teams and environments.
Help evolve the platform toward self-service environments, clearer ownership of operations by product teams, and an SRE‑style operating model.
Define and improve standards for cloud infrastructure, deployment, observability, and operational readiness.
Design and operate scalable, secure, and cost‑efficient cloud platforms.
Work with Infrastructure‑as‑Code, Kubernetes, containers, CI/CD, and automation frameworks.
Improve monitoring, alerting, tracing, SLOs, disaster recovery, and incident‑management practices.
Lead root‑cause analysis and drive sustainable post‑incident improvements.
Build security and compliance into infrastructure and delivery processes, including least‑privilege access, identity federation and OIDC, short‑lived credentials, secrets management and rotation, auditability, certificate management, supply‑chain security, and policy‑as‑code.
Implement automated testing, linting, validation, and rollback processes for infrastructure and delivery pipelines.
Use AI agents responsibly to improve infrastructure design, automation, incident analysis, and operational workflows while applying strong engineering judgement and quality guardrails.
Act as a technical partner to Engineering, Product, Security, and IT leadership.
Mentor engineers, improve documentation and standards, and contribute to the long‑term direction of DevOps practices.

Requirements

Strong professional experience in DevOps, platform engineering, infrastructure engineering, or a related field; typically 5+ years.
Proven experience leading infrastructure or delivery initiatives beyond a single team or service.
Strong knowledge of AWS or a comparable cloud platform, ideally including multi‑account environments.
Expert‑level experience with Kubernetes, containers, Infrastructure‑as‑Code, and cloud automation.
Hands‑on experience with Terraform, Helm, GitHub Actions, and GitOps tools such as Argo CD.
Experience with production observability, including metrics, logs, traces, alerting, and platforms such as SigNoz.
Experience designing reliable systems and improving operational readiness, observability, and incident response.
Understanding of infrastructure security, compliance, identity, access management, and supply‑chain security.
Experience creating automated testing, linting, validation, and rollback mechanisms for infrastructure or delivery pipelines.
Strong problem‑solving skills and the ability to lead root‑cause analysis for complex incidents.
Ability to communicate clearly with both technical and non‑technical stakeholders.
Professional fluency in English.
Ability to use AI tools effectively to deliver fast results and create reliable, repeatable workflows that improve the reliability and stability of systems.

Nice to have

Experience with cost governance and cloud‑financial management.
Experience with SLOs, error budgets, disaster recovery, and reliability engineering.
Experience with policy‑as‑code and automated security scanning.
Strong scripting or programming experience, for example Python, Go, or Bash.
Experience introducing AI‑assisted engineering workflows with appropriate safeguards.
Experience supporting AI‑platform or hybrid‑cloud workloads, including GPU scheduling or cluster operations with tools such as Karpenter, Cilium, or KubeRay.
Experience mentoring engineers or contributing to organisation‑wide technical standards.